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Admin Credential Abuse: How Darktrace Succeeded Where Other Solutions Failed

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17
Aug 2023
17
Aug 2023
This blog details a malicious actor’s attempt to abuse a customer’s administrative credentials in order to further their compromise on the network. Thanks to its anomaly-based approach to threat detection, Darktrace was the only solution in the customer’s stack to identify and contain the attack.

What is Admin Credential Abuse?

In an effort to remain undetected by increasingly vigilant security teams, malicious actors across the threat landscape often resort to techniques that allow them to remain ‘quiet’ on the network and carry out their objectives subtly. One such technique often employed by attackers is using highly privileged credentials to carry out malicious activity.

This emphasizes the need to be hyper vigilant and not assume that ‘administrative’ activity using privileged credentials is legitimate. In this way, both internal visibility and defense in-depth are needed, as well as a strong understanding of ‘normal’ administrative activity to then identify any deviations from this.  

In one recent example, Darktrace identified a threat actor attempting to use privileged administrative credentials to move laterally through a customer’s network and compromise two further critical servers. Darktrace DETECT™ identified that this activity was unusual and alerted the customer to early signs of compromise, reconnaissance and lateral movement to the other critical devices, while Darktrace RESPOND™ acted autonomously to inhibit the spread of activity and allowed the customer to quarantine the compromised devices.

Attack Overview and Darktrace Coverage

Over the course of a week in late May 2023, Darktrace observed a compromise on the network of a customer in the Netherlands. The threat actors primarily used living off the land techniques, abusing legitimate administrative credentials and executables to perform unexpected activities. This technique is intended to go under the radar of traditional security tools that are often unable to distinguish between the legitimate or malicious use of privileged credentials.

Darktrace was the only security solution in the customer’s stack that way able to detect and contain the attack, preventing it from spreading through their digital estate.

1. Device Reactivated

On May 22, 2023, Darktrace began to observe traffic originating from a File Server device which prior to this, had been been inactive on the network for some time, with no incoming or outgoing traffic recently observed for this IP. Therefore, upon initiating connections again, Darktrace’s AI tagged the device with the “Re-Activated Device” label. It also tagged the device as an “Internet Facing System”, which could represent an initial point of compromise.

Following this, the device was observed using an administrative credential that was commonly used across network, with no clear indications of brute-force activity or successive login failures preceeding this activity. The unusual use of a known credential on a network can be very difficult to detect for traditional security tools. Darktrace’s anomaly-based detection allows it to recognize subtle deviations in device behavior meaning it is uniquely placed to recognize this type of activity.

2. Reconaissance  

On the following day, the affected device began to perform SMB scans for open 445 ports, and writing files such as srvsvc and winreg, both of which are indicative of network  reconnaissance. Srvsvc is used to enumerate available SMB shares on destination devices which could be used to then write malicious files to these shares, while Winreg (Windows Registry) is used to store information that configures users, applications, and hardware devices [1]. Darktrace also observed the device carrying out DCE_RPC activity and making Windows Management Instrumentation (WMI) enumeration requests to other internal devices.

3. Lateral Movement via SMB

On May 24 and May 30, Darktrace observed the same device writing files over SMB to a number of other internal devices, including an SMB server and the Domain Controller. Darktrace identified that these writers were to privileged credential paths, such as C$ and ADMIN$, and it further recognized that the device was using the compromised administrative credential.

The files included remote command executable files (.exe) and batch scripts which execute commands upon clicking in a serial order. This behavior is indicative of a threat actor performing lateral movement in an attempt to infect other devices and strengthen their foothold in the network.

Files written:

·       LogConverter.bat

·       sql.bat

·       Microsoft.NodejsTools.PressAnyKey.exe

·       PSEXESVC.exe

·       Microsoft.NodejsTools.PressAnyKey.lnk

·       CG6oDkyFHl3R.t

5. Reconnaissance Spread

Around the same time as the observed lateral movement activity, between May 24 and May 30, the initially compromised device continued SMB and DCE_RPC activity, mainly involving SMB writes of files such as srvsvc, and PSEXESVC.exe.

Then, on May 28, Darktrace identified another internal Domain Controller engaging in similar suspicious behavior to the original compromised device. This included network scanning, enumeration and service control activity, indicating a spread of further malicious reconnaissance.

Following the successful detection of this activity, Darktrace’s Cyber AI Analyst launched autonomous investigations which was able to correlate incidents from multiple affected devices across the network, in doing so connecting multiple incidents into one security event.

Figure 1: Cyber AI Analyst connecting multiple events into one incident
Figure 2: Cyber AI Analyst investigation process to identify suspicious activity.

6. Lateral Movement

Alongside these SMB writes, the initially compromised device was seen connecting to various internal devices over ports associated with administrative protocols such as Remote Desktop Protocol (RDP). It also made a high volume of NTLM login failures for the credential ‘administrator’, suggesting that the malicious actor was attempting to brute-force an administrative credential.

7. Suspicious External Activity

Following earlier SMB writes from the initially compromised device to the Domain Controller server, the Domain Controller was seen making an unusual volume of external connections to rare endpoints which could indicate malicious command and control (C2) communication.

Alongside this activity, between May 30 and June 1, Darktrace also observed an unusually large number (over 12 million) of incoming connections from external endpoints. This activity is likely indicative of an attempted Denial of Service (DoS) attack.

Endpoints include:

·       45.15.145[.]92

·       198.2.200[.]89

·       162.211.180[.]215

Figure 3: Graphing function in the Darktrace UI showing the observed spike of inbound communication from external endpoints, indicating a potential DoS attack.

8. Reconnaissance and RDP activity

On May 31, the initially compromised device was seen creating an administrative RDP session with cookie ‘Administr’. Using the initially compromised administrative credential, further suspicious SMB activity was observed from the compromised devices on the same day including further SMB Enumeration, service control, PsExec remote command execution, and writes of another suspicious batch script file to various internal devices.

Darktrace RESPOND Coverage

Darktrace RESPOND’s autonomous response capabilities allowed it to take instantaneous preventative action against the affected devices as soon as suspicious activity was identified, consequently inhibiting the spread of this attack.

Specifically, Darktrace RESPOND was able to block suspicious connections to multiple internal devices and ports, among them port 445 which was used by threat actors to perform SMB scanning, for one hour. As a result of the autonomous actions carried out by Darktrace, the attack was stopped at the earliest possible stage.

Figure 4: Autonomous RESPOND actions taken against initially compromised devices.

In addition to these autonomous actions, the customer was able to further utilize RESPOND for containment purposes by manually actioning some of the more severe actions suggested by RESPOND, such as quarantining compromised devices from the rest of the network for a week.

Figure 5: Manually applied RESPOND actions to quarantine compromised devices for one week.

Conclusion

As attackers continue to employ harder to detect living off the land techniques to exploit administrative credentials and move laterally across networks, it is paramount for organizations to have an intelligent decision maker that can recgonize the subtle deviations in device behavior.

Thanks to its Self-Learning AI, Darktrace is uniquely placed to understand its customer’s networks, allowing it to recognize unusual or uncommon activity for individual devices or user credentials, irrespective of whether this activity is typically considered as legitimate.

In this case, Darktrace was the only solution in the customer’s security stack that successfully identified and mitigated this attack. Darktrace DETECT was able to identify the the early stages of the compromise and provide full visibility over the kill chain. Meanwhile, Darktrace RESPOND moved at machine-speed, blocking suspicious connections and preventing the compromise from spreading across the customer’s network.

Appendices

Darktrace DETECT Model Breaches

Anomalous Connection / High Volume of New or Uncommon Service Control

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / SMB Enumeration

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Unusual Admin SMB Session

Anomalous File / Internal / Executable Uploaded to DC

Anomalous File / Internal / Unusual SMB Script Write

Anomalous Server Activity / Outgoing from Server

Anomalous Server Activity / Possible Denial of Service Activity

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Compliance / Outgoing NTLM Request from DC

Compliance / SMB Drive Write

Device / Anomalous NTLM Brute Force

Device / ICMP Address Scan  

Device / Internet Facing Device with High Priority Alert

Device / Large Number of Model Breaches

Device / Large Number of Model Breaches from Critical Network Device

Device / Multiple Lateral Movement Model Breaches

Device / Network Scan

Device / New or Uncommon SMB Named Pipe

Device / New or Uncommon WMI Activity

Device / New or Unusual Remote Command Execution

Device / Possible SMB/NTLM Brute Force

Device / RDP Scan

Device / SMB Lateral Movement

Device / SMB Session Brute Force (Admin)

Device / Suspicious SMB Scanning Activity

Darktrace RESPOND Model Breaches

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Cyber AI Analyst Incidents

Extensive Suspicious Remote WMI Activity

Extensive Unusual Administrative Connections

Large Volume of SMB Login Failures from Multiple Devices

Port Scanning

Scanning of Multiple Devices

SMB Writes of Suspicious Files

Suspicious Chain of Administrative Connections

Suspicious DCE_RPC Activity

TCP Scanning of Multiple Devices

MITRE ATT&CK Mapping

RECONNAISSANCE
T1595 Active Scanning
T1589.001 Gathering Credentials

CREDENTIAL ACCESS
T1110 Brute Force

LATERAL MOVEMENT
T1210 Exploitation of Remote Services
T1021.001 Remote Desktop Protocol

COMMAND AND CONTROL
T1071 Application Layer Protocol

IMPACT
T1498.001 Direct Network Flood

References

[1] https://learn.microsoft.com/en-us/troubleshoot/windows-server/performance/windows-registry-advanced-users

INSIDE THE SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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Zoe Tilsiter
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Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

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15
May 2024

Email – the vector of choice for threat actors

In times of unprecedented globalization and internationalization, the enormous number of emails sent and received by organizations every day has opened the door for threat actors looking to gain unauthorized access to target networks.

Now, increasingly global organizations not only need to safeguard their email environments against phishing campaigns targeting their employees in their own language, but they also need to be able to detect malicious emails sent in foreign languages too [1].

Why are non-English language phishing emails more popular?

Many traditional email security vendors rely on pre-trained English language models which, while function adequately against malicious emails composed in English, would struggle in the face of emails composed in other languages. It should, therefore, come as no surprise that this limitation is becoming increasingly taken advantage of by attackers.  

Darktrace/Email™, on the other hand, focuses on behavioral analysis and its Self-Learning AI understands what is considered ‘normal’ for every user within an organization’s email environment, bypassing any limitations that would come from relying on language-trained models [1].

In March 2024, Darktrace observed anomalous emails on a customer’s network that were sent from email addresses belonging to an international fast-food chain. Despite this seeming legitimacy, Darktrace promptly identified them as phishing emails that contained malicious payloads, preventing a potentially disruptive network compromise.

Attack Overview and Darktrace Coverage

On March 3, 2024, Darktrace observed one of the customer’s employees receiving an email which would turn out to be the first of more than 50 malicious emails sent by attackers over the course of three days.

The Sender

Darktrace/Email immediately understood that the sender never had any previous correspondence with the organization or its employees, and therefore treated the emails with caution from the onset. Not only was Darktrace able to detect this new sender, but it also identified that the emails had been sent from a domain located in China and contained an attachment with a Chinese file name.

The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.
Figure 1: The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.

Darktrace further detected that the phishing emails had been sent in a synchronized fashion between March 3 and March 5. Eight unique senders were observed sending a total of 55 emails to 55 separate recipients within the customer’s email environment. The format of the addresses used to send these suspicious emails was “12345@fastflavor-shack[.]cn”*. The domain “fastflavor-shack[.]cn” is the legitimate domain of the Chinese division of an international fast-food company, and the numerical username contained five numbers, with the final three digits changing which likely represented different stores.

*(To maintain anonymity, the pseudonym “Fast Flavor Shack” and its fictitious domain, “fastflavor-shack[.]cn”, have been used in this blog to represent the actual fast-food company and the domains identified by Darktrace throughout this incident.)

The use of legitimate domains for malicious activities become commonplace in recent years, with attackers attempting to leverage the trust endpoint users have for reputable organizations or services, in order to achieve their nefarious goals. One similar example was observed when Darktrace detected an attacker attempting to carry out a phishing attack using the cloud storage service Dropbox.

As these emails were sent from a legitimate domain associated with a trusted organization and seemed to be coming from the correct connection source, they were verified by Sender Policy Framework (SPF) and were able to evade the customer’s native email security measures. Darktrace/Email; however, recognized that these emails were actually sent from a user located in Singapore, not China.

Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.
Figure 2: Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.

The Emails

Darktrace/Email autonomously analyzed the suspicious emails and identified that they were likely phishing emails containing a malicious multistage payload.

Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.
Figure 3: Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.

There has been a significant increase in multistage payload attacks in recent years, whereby a malicious email attempts to elicit recipients to follow a series of steps, such as clicking a link or scanning a QR code, before delivering a malicious payload or attempting to harvest credentials [2].

In this case, the malicious actor had embedded a suspicious link into a QR code inside a Microsoft Word document which was then attached to the email in order to direct targets to a malicious domain. While this attempt to utilize a malicious QR code may have bypassed traditional email security tools that do not scan for QR codes, Darktrace was able to identify the presence of the QR code and scan its destination, revealing it to be a suspicious domain that had never previously been seen on the network, “sssafjeuihiolsw[.]bond”.

Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.
Figure 4: Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.

At the time of the attack, there was no open-source intelligence (OSINT) on the domain in question as it had only been registered earlier the same day. This is significant as newly registered domains are typically much more likely to bypass gateways until traditional security tools have enough intelligence to determine that these domains are malicious, by which point a malicious actor may likely have already gained access to internal systems [4]. Despite this, Darktrace’s Self-Learning AI enabled it to recognize the activity surrounding these unusual emails as suspicious and indicative of a malicious phishing campaign, without needing to rely on existing threat intelligence.

The most commonly used sender name line for the observed phishing emails was “财务部”, meaning “finance department”, and Darktrace observed subject lines including “The document has been delivered”, “Income Tax Return Notice” and “The file has been released”, all written in Chinese.  The emails also contained an attachment named “通知文件.docx” (“Notification document”), further indicating that they had been crafted to pass for emails related to financial transaction documents.

 Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.
Figure 5: Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.

Conclusion

Although this phishing attack was ultimately thwarted by Darktrace/Email, it serves to demonstrate the potential risks of relying on solely language-trained models to detect suspicious email activity. Darktrace’s behavioral and contextual learning-based detection ensures that any deviations in expected email activity, be that a new sender, unusual locations or unexpected attachments or link, are promptly identified and actioned to disrupt the attacks at the earliest opportunity.

In this example, attackers attempted to use non-English language phishing emails containing a multistage payload hidden behind a QR code. As traditional email security measures typically rely on pre-trained language models or the signature-based detection of blacklisted senders or known malicious endpoints, this multistage approach would likely bypass native protection.  

Darktrace/Email, meanwhile, is able to autonomously scan attachments and detect QR codes within them, whilst also identifying the embedded links. This ensured that the customer’s email environment was protected against this phishing threat, preventing potential financial and reputation damage.

Credit to: Rajendra Rushanth, Cyber Analyst, Steven Haworth, Head of Threat Modelling, Email

Appendices  

List of Indicators of Compromise (IoCs)  

IoC – Type – Description

sssafjeuihiolsw[.]bond – Domain Name – Suspicious Link Domain

通知文件.docx – File - Payload  

References

[1] https://darktrace.com/blog/stopping-phishing-attacks-in-enter-language  

[2] https://darktrace.com/blog/attacks-are-getting-personal

[3] https://darktrace.com/blog/phishing-with-qr-codes-how-darktrace-detected-and-blocked-the-bait

[4] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

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The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

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13
May 2024

About the AI Cybersecurity Report

Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on cybersecurity solutions.

To access the full report, click here.

The effects of AI on cybersecurity solutions

Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.                  

The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.  

Do security professionals believe AI will impact their security operations?

Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.                                                                

Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.

This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.

There are many other types of AI, which can be applied to many other use cases:

Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.

Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.

Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.

What are the areas of cybersecurity AI will impact the most?

Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.                                                                                  

The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.

Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.

Where will defensive AI have the greatest impact on cybersecurity?

Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.        

Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations,
and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.

Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.

Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.

How will security teams stop AI-powered threats?            

Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.

There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.

There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.

On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.  

To access the full report, click here.

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